Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case
In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks app...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/2/364 |
| _version_ | 1797413036877676544 |
|---|---|
| author | Emma Viviani Luca Di Persio Matthias Ehrhardt |
| author_facet | Emma Viviani Luca Di Persio Matthias Ehrhardt |
| author_sort | Emma Viviani |
| collection | DOAJ |
| description | In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data. |
| first_indexed | 2024-03-09T05:11:45Z |
| format | Article |
| id | doaj.art-296c87211bd84af7bedcd925ff6883e2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T05:11:45Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-296c87211bd84af7bedcd925ff6883e22023-12-03T12:48:21ZengMDPI AGEnergies1996-10732021-01-0114236410.3390/en14020364Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German CaseEmma Viviani0Luca Di Persio1Matthias Ehrhardt2Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, ItalyDepartment of Computer Science, College of Mathematics, University of Verona, 37134 Verona, ItalyDepartment of Applied Mathematics and Numerical Analysis, University of Wuppertal, 42119 Wuppertal, GermanyIn this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.https://www.mdpi.com/1996-1073/14/2/364electricity pricestatistical methodautoregressiveprobabilistic forecastneural network |
| spellingShingle | Emma Viviani Luca Di Persio Matthias Ehrhardt Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case Energies electricity price statistical method autoregressive probabilistic forecast neural network |
| title | Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case |
| title_full | Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case |
| title_fullStr | Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case |
| title_full_unstemmed | Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case |
| title_short | Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case |
| title_sort | energy markets forecasting from inferential statistics to machine learning the german case |
| topic | electricity price statistical method autoregressive probabilistic forecast neural network |
| url | https://www.mdpi.com/1996-1073/14/2/364 |
| work_keys_str_mv | AT emmaviviani energymarketsforecastingfrominferentialstatisticstomachinelearningthegermancase AT lucadipersio energymarketsforecastingfrominferentialstatisticstomachinelearningthegermancase AT matthiasehrhardt energymarketsforecastingfrominferentialstatisticstomachinelearningthegermancase |